sigmaqut: Estimation of sigma based on the Quantile Universal Threshold

In qut: Quantile Universal Threshold

Description

Estimation of σ using a two layer estimation scheme as in Refitted Cross Validation, by performing variable selection with the Quantile Universal Threshold, and obtaining the two estimations of sigma with the ordinary least squares estimator.

Usage

sigmaqut(y, X, estimator = "unbiased", intercept = TRUE, 
alpha.level = "default", M = 1000, qut.standardize = TRUE, 
penalty.factor = rep(1, p), offset = NULL, ...)

Arguments

y	response variable. Quantitative for family=gaussian, or family=poisson (non-negative counts). For family=binomial should be a factor with two levels.
X	input matrix, of dimension n x p; each row is an observation vector.
estimator	type of estimation of sigma when sigma = 'qut'. It can be equal to 'unbiased' (standard unbiased formula), or 'mle' (maximum likelihood formula).
intercept	should intercept(s) be fitted (default=TRUE) or set to zero (FALSE).
alpha.level	level, such that quantile τ=(1-alpha.level)/γ. Default is 1/(√{π\log(p)}).
M	number of Monte Carlo Simulations to estimate the distribution Λ. Default is 1000.
qut.standardize	standardize matrix X with a quantile-based standardization. Default is TRUE.
penalty.factor	separate penalty factors can be applied to each coefficient. As in qut.
offset	a vector of length n that is included in the linear predictor. As in qut.
...	other glmnet options.

Value

Estimator of σ

Note

lambdaqut,qut

Author(s)

Jairo Diaz

qut documentation built on Jan. 19, 2021, 5:09 p.m.